Another approach is to split the pooled dataset \(\lbrace u_i\rbrace _{i=1}^{n+m}\)  into training and
testing sets. A classifier is then trained on the former, and the accuracy is measured
for the latter. The accuracy becomes the test statistic \(\hat{t}\)  ,
which follows asymptotically \(N(\frac{1}{2}, \frac{1}{4 n_{test}})\)  [1]}. Alternatively, a permutation test can be used instead [2]}.
Two disadvantages of this kind of test are that they can not use the full sample for computing
the test statistic — therefore, they are not suitable for small data sets —
and they are underpowered due to the discrete nature of the test statistic [3]}.
